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Di Bartolomeo A. Advanced Field-Effect Sensors. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094554. [PMID: 37177758 PMCID: PMC10181658 DOI: 10.3390/s23094554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Accepted: 05/05/2023] [Indexed: 05/15/2023]
Abstract
Sensors based on the field-effect principle have been used for more than fifty years in a variety of applications ranging from bio-chemical sensing to radiation detection or environmental parameter monitoring [...].
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Affiliation(s)
- Antonio Di Bartolomeo
- Physics Department E.R. Caianiello, University of Salerno, 84084 Fisciano, Salerno, Italy
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Feng H, Gonzalez Viejo C, Vaghefi N, Taylor PWJ, Tongson E, Fuentes S. Early Detection of Fusarium oxysporum Infection in Processing Tomatoes ( Solanum lycopersicum) and Pathogen-Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling. SENSORS (BASEL, SWITZERLAND) 2022; 22:8645. [PMID: 36433241 PMCID: PMC9693623 DOI: 10.3390/s22228645] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 11/05/2022] [Accepted: 11/08/2022] [Indexed: 06/16/2023]
Abstract
The early detection of pathogen infections in plants has become an important aspect of integrated disease management. Although previous research demonstrated the idea of applying digital technologies to monitor and predict plant health status, there is no effective system for detecting pathogen infection before symptomatology appears. This paper presents the use of a low-cost and portable electronic nose coupled with machine learning (ML) models for early disease detection. Several artificial neural network models were developed to predict plant physiological data and classify processing tomato plants and soil samples according to different levels of pathogen inoculum by using e-nose outputs as inputs, plant physiological data, and the level of infection as targets. Results showed that the pattern recognition models based on different infection levels had an overall accuracy of 94.4-96.8% for tomato plants and between 94.81% and 96.22% for soil samples. For the prediction of plant physiological parameters (photosynthesis, stomatal conductance, and transpiration) using regression models or tomato plants, the overall correlation coefficient was 0.97-0.99, with very significant slope values in the range 0.97-1. The performance of all models shows no signs of under or overfitting. It is hence proven accurate and valid to use the electronic nose coupled with ML modeling for effective early disease detection of processing tomatoes and could also be further implemented to monitor other abiotic and biotic stressors.
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Affiliation(s)
- Hanyue Feng
- Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Claudia Gonzalez Viejo
- Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Niloofar Vaghefi
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Paul W. J. Taylor
- School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Eden Tongson
- Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
| | - Sigfredo Fuentes
- Digital Agriculture Food and Wine Group, School of Agriculture and Food, Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Parkville, VIC 3010, Australia
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